16 research outputs found
What changed your mind : the roles of dynamic topics and discourse in argumentation process
In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the in- creasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful -- topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations
Personal Entity, Concept, and Named Entity Linking in Conversations
Building conversational agents that can have natural and knowledge-grounded
interactions with humans requires understanding user utterances. Entity Linking
(EL) is an effective and widely used method for understanding natural language
text and connecting it to external knowledge. It is, however, shown that
existing EL methods developed for annotating documents are suboptimal for
conversations, where personal entities (e.g., "my cars") and concepts are
essential for understanding user utterances. In this paper, we introduce a
collection and a tool for entity linking in conversations. We collect EL
annotations for 1327 conversational utterances, consisting of links to named
entities, concepts, and personal entities. The dataset is used for training our
toolkit for conversational entity linking, CREL. Unlike existing EL methods,
CREL is developed to identify both named entities and concepts. It also
utilizes coreference resolution techniques to identify personal entities and
references to the explicit entity mentions in the conversations. We compare
CREL with state-of-the-art techniques and show that it outperforms all existing
baselines
Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions
In online communities, antisocial behavior such as trolling disrupts
constructive discussion. While prior work suggests that trolling behavior is
confined to a vocal and antisocial minority, we demonstrate that ordinary
people can engage in such behavior as well. We propose two primary trigger
mechanisms: the individual's mood, and the surrounding context of a discussion
(e.g., exposure to prior trolling behavior). Through an experiment simulating
an online discussion, we find that both negative mood and seeing troll posts by
others significantly increases the probability of a user trolling, and together
double this probability. To support and extend these results, we study how
these same mechanisms play out in the wild via a data-driven, longitudinal
analysis of a large online news discussion community. This analysis reveals
temporal mood effects, and explores long range patterns of repeated exposure to
trolling. A predictive model of trolling behavior shows that mood and
discussion context together can explain trolling behavior better than an
individual's history of trolling. These results combine to suggest that
ordinary people can, under the right circumstances, behave like trolls.Comment: Best Paper Award at CSCW 201
User Engagement and the Toxicity of Tweets
Twitter is one of the most popular online micro-blogging and social
networking platforms. This platform allows individuals to freely express
opinions and interact with others regardless of geographic barriers. However,
with the good that online platforms offer, also comes the bad. Twitter and
other social networking platforms have created new spaces for incivility. With
the growing interest on the consequences of uncivil behavior online,
understanding how a toxic comment impacts online interactions is imperative. We
analyze a random sample of more than 85,300 Twitter conversations to examine
differences between toxic and non-toxic conversations and the relationship
between toxicity and user engagement. We find that toxic conversations, those
with at least one toxic tweet, are longer but have fewer individual users
contributing to the dialogue compared to the non-toxic conversations. However,
within toxic conversations, toxicity is positively associated with more
individual Twitter users participating in conversations. This suggests that
overall, more visible conversations are more likely to include toxic replies.
Additionally, we examine the sequencing of toxic tweets and its impact on
conversations. Toxic tweets often occur as the main tweet or as the first
reply, and lead to greater overall conversation toxicity. We also find a
relationship between the toxicity of the first reply to a toxic tweet and the
toxicity of the conversation, such that whether the first reply is toxic or
non-toxic sets the stage for the overall toxicity of the conversation,
following the idea that hate can beget hate
Analyzing the Persuasive Effect of Style in News Editorial Argumentation
News editorials argue about political issues in order to challenge or reinforce the stance of readers with different ideologies. Previous research has investigated such persuasive effects for argumentative content. In contrast, this paper studies how important the style of news editorials is to achieve persuasion. To this end, we first compare content- and style-oriented classifiers on editorials from the liberal NYTimes with ideology-specific effect annotations. We find that conservative readers are resistant to NYTimes style, but on liberals, style even has more impact than content. Focusing on liberals, we then cluster the leads, bodies, and endings of editorials, in order to learn about writing style patterns of effective argumentation